Constrained deep reinforcement learning for energy sustainable multi-UAV based random access IoT networks with NOMA
In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) technique to improve
the massive channel access of a wireless IoT network where solar-powered Unmanned …
the massive channel access of a wireless IoT network where solar-powered Unmanned …
Reinforcement learning for decentralized trajectory design in cellular UAV networks with sense-and-send protocol
Recently, the unmanned aerial vehicles (UAVs) have been widely used in real-time sensing
applications over cellular networks. The performance of a UAV is determined by both its …
applications over cellular networks. The performance of a UAV is determined by both its …
Resource allocation and trajectory design in UAV-aided cellular networks based on multiagent reinforcement learning
In this article, we focus on a downlink cellular network, where multiple unmanned aerial
vehicles (UAVs) serve as aerial base stations for ground users through frequency-division …
vehicles (UAVs) serve as aerial base stations for ground users through frequency-division …
Scalable and cooperative deep reinforcement learning approaches for multi-UAV systems: A systematic review
In recent years, the use of multiple unmanned aerial vehicles (UAVs) in various applications
has progressively increased thanks to advancements in multi-agent system technology …
has progressively increased thanks to advancements in multi-agent system technology …
Cooperative trajectory design of multiple UAV base stations with heterogeneous graph neural networks
Unmanned aerial vehicles as base stations (UAV-BSs) are recognized as effective means
for tackling eruptive communication service requirements especially when terrestrial …
for tackling eruptive communication service requirements especially when terrestrial …
Multiagent Q-Learning-Based Multi-UAV Wireless Networks for Maximizing Energy Efficiency: Deployment and Power Control Strategy Design
In air-to-ground communications, the network lifetime depends on the operation time of
unmanned aerial vehicle-base stations (UAV-BSs) owing to the restricted battery capacity …
unmanned aerial vehicle-base stations (UAV-BSs) owing to the restricted battery capacity …
Leveraging UAVs for coverage in cell-free vehicular networks: A deep reinforcement learning approach
The success in transitioning towards smart cities relies on the availability of information and
communication technologies that meet the demands of this transformation. The terrestrial …
communication technologies that meet the demands of this transformation. The terrestrial …
Efficient drone mobility support using reinforcement learning
Flying drones can be used in a wide range of applications and services from surveillance to
package delivery. To ensure robust control and safety of drone operations, cellular networks …
package delivery. To ensure robust control and safety of drone operations, cellular networks …
Distributed energy-efficient multi-UAV navigation for long-term communication coverage by deep reinforcement learning
In this paper, we aim to design a fully-distributed control solution to navigate a group of
unmanned aerial vehicles (UAVs), as the mobile Base Stations (BSs) to fly around a target …
unmanned aerial vehicles (UAVs), as the mobile Base Stations (BSs) to fly around a target …
Three-dimension trajectory design for multi-UAV wireless network with deep reinforcement learning
The effective trajectory design of multiple unmanned aerial vehicles (UAVs) is investigated
for improving the capacity of the communication system. The aim is for maximizing real-time …
for improving the capacity of the communication system. The aim is for maximizing real-time …